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An Efficient Dimensionality Reduction Method for the Classification of Satellite Remote Sensing Hyperspectral Images

by Md. Rashedul Islam, Ayasha Siddiqa, Nafisa Tasnim
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 14
Year of Publication: 2021
Authors: Md. Rashedul Islam, Ayasha Siddiqa, Nafisa Tasnim
10.5120/ijca2021921458

Md. Rashedul Islam, Ayasha Siddiqa, Nafisa Tasnim . An Efficient Dimensionality Reduction Method for the Classification of Satellite Remote Sensing Hyperspectral Images. International Journal of Computer Applications. 183, 14 ( Jul 2021), 22-28. DOI=10.5120/ijca2021921458

@article{ 10.5120/ijca2021921458,
author = { Md. Rashedul Islam, Ayasha Siddiqa, Nafisa Tasnim },
title = { An Efficient Dimensionality Reduction Method for the Classification of Satellite Remote Sensing Hyperspectral Images },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2021 },
volume = { 183 },
number = { 14 },
month = { Jul },
year = { 2021 },
issn = { 0975-8887 },
pages = { 22-28 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume183/number14/31995-2021921458/ },
doi = { 10.5120/ijca2021921458 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:16:48.272623+05:30
%A Md. Rashedul Islam
%A Ayasha Siddiqa
%A Nafisa Tasnim
%T An Efficient Dimensionality Reduction Method for the Classification of Satellite Remote Sensing Hyperspectral Images
%J International Journal of Computer Applications
%@ 0975-8887
%V 183
%N 14
%P 22-28
%D 2021
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Finding an informative subset of features from the original hyperspectral images has become essential because of its wide applications in ground object identification. However, information extraction from hyperspectral images is becoming challenging because of its high correlation among the image bands and spectral and spatial redundancy. This paper proposed a feature reduction approach, combining both feature extraction and feature selection. A combination of Minimum Noise Fraction (MNF) and information-based measure, cross cumulative residual entropy (CCRE), is proposed to select the subset of features from the original image to obtain improved classification accuracy. In the proposed method, feature ranking is improved by scaling the CCRE to a specific range to avoid redundant features. The proposed technique (MNF-nCCRE) is tested on two hyperspectral images captured by the NASA AVIRIS sensor and HYDICE sensor. The experimental results typically indicate a noticeable improvement in terms of classification accuracy. The proposed technique shows 96.8%, and 99.10% classification accuracy on AVIRIS and HYDICE hyperspectral data, respectively, higher than the standard approaches studied.

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Index Terms

Computer Science
Information Sciences

Keywords

Feature extraction subspace identification minimum noise fraction AVIRIS HYDICE hyperspectral images classification.

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